Home Security System Research Articles

IOT-Based Smart Door Selector For Double Security: Integration of Rfid and Blynk App for Economical Solution

This research aims to design and develop an Internet of Things (IoT)-based smart door lock system by utilizing Radio Frequency Identification (RFID) technology and the Blynk application. The system is designed to improve the level of home security by providing a variety of flexible access methods, including the use of RFID cards, smartphone apps, and emergency buttons. The prototype method is applied in system development, which involves creating a physical model to evaluate its performance and functionality. The test results showed that the system was operating properly, where RFID only responded to the registered card to unlock, thus increasing access security. Hardware design that is carried out with an economical approach and simple use of components makes this system more affordable compared to commercial smart door lock products on the market. This research shows great potential in increasing efficiency and comfort in managing home security systems and offers ease of implementation in various types of housing. Thus, this research makes a significant contribution to the development of innovative and practical solutions to improve home safety at a lower cost while providing a sense of security for its occupants.

Deep-Learning-Based Real-Time Passive Non-Line-of-Sight Imaging for Room-Scale Scenes.

Non-line-of-sight imaging is a technique for reconstructing scenes behind obstacles. We report a real-time passive non-line-of-sight (NLOS) imaging method for room-scale hidden scenes, which can be applied to smart home security monitoring sensing systems and indoor fast fuzzy navigation and positioning under the premise of protecting privacy. An unseen scene encoding enhancement network (USEEN) for hidden scene reconstruction is proposed, which is a convolutional neural network designed for NLOS imaging. The network is robust to ambient light interference conditions on diffuse reflective surfaces and maintains a fast reconstruction speed of 12.2 milliseconds per estimation. The consistency of the mean square error (MSE) is verified, and the peak signal-to-noise ratio (PSNR) values of 19.21 dB, 15.86 dB, and 13.62 dB are obtained for the training, validation, and test datasets, respectively. The average values of the structural similarity index (SSIM) are 0.83, 0.68, and 0.59, respectively, and are compared and discussed with the corresponding indicators of the other two models. The sensing system built using this method will show application potential in many fields that require accurate and real-time NLOS imaging, especially smart home security systems in room-scale scenes.

Real-Time Home Automation System Using BCI Technology.

A Brain-Computer Interface (BCI) processes and converts brain signals to provide commands to output devices to carry out certain tasks. The main purpose of BCIs is to replace or restore the missing or damaged functions of disabled people, including in neuromuscular disorders like Amyotrophic Lateral Sclerosis (ALS), cerebral palsy, stroke, or spinal cord injury. Hence, a BCI does not use neuromuscular output pathways; it bypasses traditional neuromuscular pathways by directly interpreting brain signals to command devices. Scientists have used several techniques like electroencephalography (EEG) and intracortical and electrocorticographic (ECoG) techniques to collect brain signals that are used to control robotic arms, prosthetics, wheelchairs, and several other devices. A non-invasive method of EEG is used for collecting and monitoring the signals of the brain. Implementing EEG-based BCI technology in home automation systems may facilitate a wide range of tasks for people with disabilities. It is important to assist and empower individuals with paralysis to engage with existing home automation systems and gadgets in this particular situation. This paper proposes a home security system to control a door and a light using an EEG-based BCI. The system prototype consists of the EMOTIV Insight™ headset, Raspberry Pi 4, a servo motor to open/close the door, and an LED. The system can be very helpful for disabled people, including arm amputees who cannot close or open doors or use a remote control to turn on or turn off lights. The system includes an application made in Flutter to receive notifications on a smartphone related to the status of the door and the LEDs. The disabled person can control the door as well as the LED using his/her brain signals detected by the EMOTIV Insight™ headset.

Development of Internet of Things-Based Room Security using Telegram

Security plays a very important role in various aspects, one of which is the security of a home room which is a building for a residence. Currently, there are still many home security system devices circulating on the market that are considered ineffective in their use and cost. In addition, the home security system cannot provide warning notifications when home security is threatened and send the latest conditions of the house. This study designs a home room security system using the Internet of Things (IoT) based on Telegram Messenger. When the Passive Infrared Receiver (PIR) sensor detects movement, the OV2460 camera on the ESP32-Cam module will take a picture and then send it to the homeowner as a form of home security warning via a bot on Telegram Messenger. When the Flame Sensor detects a fire, the Buzzer will sound and then send a notification in the form of a fire warning. The results of this study showed that the home room security that was created was able to detect movement at an effective distance of the PIR sensor of 1 - 6 m and the presence of fire on the Flame Sensor which has a wavelength between 760nm - 1100nm. For the next researcher, they can develop a camera that can detect faces (face detection) and face recognition.

Cost-Efficient Automated Intrusion Detection and Reporting System for Homes in Nigeria

Substantial investments are made to mitigate the persistent threat to lives and assets posed by various forms of intrusion. Automated security systems have emerged as crucial tools for safeguarding homes against intrusions in recent times. A major advantage of this is its independent ability to report human activities around homes without direct observation. However, the cost of implementing the system is not pocket-friendly to an average Nigerian given the prevailing economic situation. This research therefore seeks to develop and implement an intrusion detection and reporting system using low cost materials while maintaining a balance between quality and cost at the same time. The system architecture employs the passive infrared sensor (PIR) for motion detection, the ultrasonic sensor measures intruders’ location from the home entrance while the NodeMCU ESP8266 microcontroller is responsible for coordinating the reception and relaying of signals within the system. The system was implemented with varying distances of human presence from the security device. Reports of potential intrusion alerts were obtained within reasonable time frames (between 11 seconds and 49 seconds) on the homeowner’s mobile phone via the Blynk application. This performance demonstrates the reliability of the system for home security. The system is equally cost-efficient relative to most similar state-of-the art IoT based home security systems considered in this work. Our work therefore contributes to knowledge by proposing an affordable home security solution for a low budget Nigerian user.

Sistem Keamanan Rumah Berbasis Internet of Things Menggunakan Metode Rapid Application Development

Conventional home security systems often lack the ability to monitor and respond in real-time, which makes them vulnerable to security breaches and delayed interventions. This research is an Internet of Things (IoT) based home security system that uses hard programming methods. This system consists of hardware that uses Arduino Uno, ESP32CAM and PIR sensor modules to detect movement, an ESP32CAM camera that takes pictures, and an Arduino Uno board that processes and transmits data. ESP32CAM takes images from the PIR sensor and sends them to the cloud server when it detects motion. The smartphone application then allows users to find out about potential disruptions in real-time through photo notifications. The system's ability to provide immediate visual evidence of suspicious activity, allowing for a rapid and informed response, determines the system's effectiveness. This system is also cheap and easy to use, so a wide range of users can use it. The use of easily accessible components and open-source software reduces costs and simplifies setup, enabling widespread adoption of this updated security product

Earthquake detection in mountainous homes using the internet of things connected to photovoltaic energy supply

The North Sumatra region is an area with the potential for earthquakes originating from volcanic and oceanic eruptions which have resulted in many fatalities. Therefore, through the application of automatic monitoring and control system technology connected to the internet of things (IoTs), it is the right solution to provide efforts to increase security for residents of the house to always be vigilant. The security enhancement method referred to in this study is a home security system protection system by anticipating earthquakes. The advantage of this tool is that it applies a notification security system method with a sensitivity sensor which is automatically sent via email and sonor buzzer which also acts as sound vibrations due to an earthquake. The test results show that when a vibration occurs, the system will send a short email message to the user's smartphone so that the user will receive an email in the form of a warning message that the state of the house has an earthquake and the light-emitting diode (LED) interrupts and the buzzer is also on so that the alarm sounds which has been integrated into IoT. Then an integrated security monitoring system using the web can be monitored in real time.

Rancang Bangun Purwarupa Simulasi Sistem Keamanan Rumah Berbasis Internet of Things

The use of information technology has spread to all fields, for example in the field of security using the Internet of Things technology. One of the places that need to be given security is the home, so that in this study prototypes of home security systems using the Internet of Things have been made with the aim of providing information to homeowners in the event of infiltration through windows or doors, gas leaks and fires. The tools used are Arduino Mega 2560 as a microcontroller, gas sensor, fire sensor, magnetic switch sensor, RFID, keypad, and selenoid. The home security system is made so that homeowners can control the condition of the home. The results of this study are that the home security systems that are made can run well and can be integrated with home security information systems. Sensors can detect gas leaks, fires, and doors or windows open when the security system is active. Homeowners can only turn off the home security system and unlock the door by using RFID tags that have registered IDs and correct passwords. Home security systems can also turn on alarms and send notifications to homeowners, fire stations and police stations in the event of a fire, gas leak or window or door that is opened when the security system is active.

Solar Powered Home Automation and Home Security System

Solar Powered Home Automation and Home Security System

GSM-Enabled Home Automation and Security System: A Comprehensive Investigation and Implementation

This research project focused on integrating home control and security through advanced Home Automation Systems. A thorough analysis and practical implementation of home automation technology were conducted, utilizing Global System for Mobile Communication (GSM) modems to manage various home appliances, including fans, air conditioning systems, and security systems. The system relied on Short Message Service (SMS) text messages for control and monitoring. Key features included an entrance security mechanism to promptly notify homeowners of unauthorized access, bolstering overall security. Additionally, an anti-theft reporting system employed alarms and SMS notifications to inform owners of security breaches. The system also integrated safety protocols for fire or gas leakage, dispatching SMS notifications and activating alarms to alert homeowners. The use of GSM technology provided wireless access to control connected devices, enhancing system efficiency and convenience. This research contributed to the smart home technologies field by presenting an advanced system that harmonized automation, security, and safety, with potential applications across diverse residential settings. While numerous home automation systems were available in the market, the significance of the proposed home automation system lay in its advanced features and comprehensive approach to integrating control and security functions. Unlike many existing systems, which may have offered basic automation capabilities, the proposed system went beyond simple automation by seamlessly integrating security measures using GSM technology. This allowed homeowners to effectively manage a wide range of home appliances, including fans, air conditioning systems, and security systems, all through the convenience of SMS text messages. Key features of the proposed system, such as the entrance security mechanism and anti-theft reporting system, addressed critical security concerns and provided homeowners with real-time notifications of unauthorized access or security breaches. Additionally, the integration of safety protocols for fire or gas leakage further enhanced the system's utility and value by prioritizing homeowner safety. Moreover, the utilization of GSM technology offered wireless access to control connected devices, significantly enhancing system efficiency and convenience compared to traditional wired systems. This wireless connectivity ensured that homeowners could remotely monitor and control their home environment from anywhere, at any time, providing them with peace of mind and control over their living space. Overall, the proposed home automation system represented a significant advancement in the field of smart home technologies by offering a comprehensive solution that harmonized automation, security, and safety. Its potential applications across diverse residential settings underscored its importance and relevance in meeting the evolving needs of modern homeowners. Index Terms: Short Message Service (SMS), GSM modems, Safety protocols, Gas leakage detection.

Development of IOT-based Smart Anti – Theft Latch

In our day to day lives, security is of utmost concern. Everyone wants to be as secured as possible. This paper develops an Internet of Things (IoT)-based smart security system and supports the concept of a "IOT close to life and easy to use”. One of the many useful applications of IoT is home security; among other things, we're leveraging it to build affordable home security systems. The IoT is a network of physically connected objects that can exchange data and communicate with one another without the need for human intervention. The simplicity of setup, low cost, and minimal maintenance are some of this system's key benefits. This system is executed with the help of Raspberry Pi 3B. Face recognition system used determines if the individual is lively enough to receive authorization. If an unauthorized access is detected then the buzzer will be triggered and an alert notification will be sent on an app which is designed for the owner which will also include a photo detected by the camera and with an option to reset the buzzer. This system comprises of dual authentication that is Radio Frequency Identification (RFID) key and a face recognition system. This approach is able to protect the homes from intrusions by continuous detection of the suspicious actions using soft computing, that is, Convolutional Neural Network (CNN) algorithm for face recognition.

AI-Based Home Security System

Home security is of paramount importance in today's world, where we rely more on technology, home security is crucial. Using technology to make homes safer and easier to control from anywhere is important. Home security is important for the occupant’s safety. In this paper, we came up with a low cost, AI based model home security system. The system has a user-friendly interface, allowing users to start model training and face detection with simple keyboard commands. Our goal is to introduce an innovative home security system using facial recognition technology. Unlike traditional systems, this system trains and saves images of friends and family members. The system scans this folder to recognize familiar faces and provides real-time monitoring. If an unfamiliar face is detected, it promptly sends an email alert, ensuring a proactive response to potential security threats.

Vision based architecture of Home Security System

Vision based architecture of Home Security System

A 2-Way Verification Process using One Time Password Key for Home Authentication System By IJISRT

The goal of this research project is to make it possible to implement extra security measures in place of employment and residence.In the suggested system, A 2- way verification technique is applied. First, the data is stored in the Electrically Erasable Programmable Read Only Memory (EEPROM) password for security. If the user enters the correct password, a randomized verification One Time Password or key is sent via paired Bluetooth and user device which is smartphone for 2 - way verification. If the entered key or OTP matches, the system will be opened and the required job may be begun. If either the key or the OTP is entered incorrectly, access is denied and the user can have a few attempts-- three chances under the suggested approach. The main objective is to develop a complete home security system using wireless devices and embedded (micro controller) technology.

IOT Rancang Bangun Alat Pengusir Hama Burung pada Padi Sawah Petani Berbasis Internet of Things (IoT)

This research aims to develop an effective and efficient bird pest repellent tool in farmers' rice fields by utilizing Internet of Things (IoT) technology. Bird pests often pose a serious threat to agricultural products, especially in rice fields. Through the application of IoT, this tool can monitor real-time bird activity around agricultural areas and take appropriate preventive measures. This tool is designed to utilize motion sensors or pir sensors, surveillance cameras, and a sound or visual signal delivery system to chase away birds. approaching agricultural areas. PIR sensors are widely used in a variety of applications, including home security systems, motion sensor lighting, and building automation. The main advantages of PIR sensors are their low power consumption and ability to provide fast response to movement, making them an efficient and effective choice for many detection applications. motion, data collected from sensors and cameras will be sent to the IoT platform with the Telegram application and which is connected via an internet connection, allowing farmers to monitor and control tools remotely via smart devices such as smartphones or computers. The main advantage of this tool is its ability to provide a quick response to the presence of bird pests, reduce agricultural yield losses and increase crop productivity. Additionally, integration with IoT provides high flexibility and connectivity, enabling continuous system optimization. designing this tool involves the hardware and software design stages. The hardware consists of a pir sensor, an esp 32 cam microcontroller, and a speaker and servo. While the software consists of Arduino Ide, Blynk and Telegram. After design, the tool is field tested for performance. Test results show that this tool is able to detect the presence of birds with good accuracy, provides a fast response in chasing them away, Telegram is able to communicate by giving commands and receiving images, Blynk can provide information on the condition of the tool when it is online and give commands to activate the servo, sound or both of them. With a significant removal success rate, this tool is successful in reducing losses caused by bird strikes in agriculture. Additionally, it has low power consumption and can be integrated with existing IoT platforms for remote monitoring and data analysis. It is hoped that this research can make a positive contribution to efforts to develop sustainable agricultural technology and improve farmers' welfare by protecting agricultural products from bird pest attacks. Thus, it is hoped that the implementation of this tool in the field can provide an innovative and efficient solution in controlling bird pests in farmers' rice fields.

Development and Assessment of Internet of Things-Driven Smart Home Security and Automation with Voice Commands

With the rapid rise of digitalization in the global economy, home security systems have become increasingly important for personal comfort and property protection. The collaboration between humans, the Internet of Things (IoT), and smart homes can be highly efficient. Interaction considers convenience, efficiency, security, responsiveness, and automation. This study aims to develop and assess IoT-based home security systems utilizing passive infrared (PIR) sensors to improve user interface, security, and automation controls using voice commands and buttons across different communication protocols. The proposed system incorporates controls for lighting and intrusion monitoring, as well as assessing both the functionality of voice commands and the precision of intruder detection via the PIR sensors. Intelligent light control and PIR intruder detection with a variable delay time for response detection are unified into the research methodology. The test outcomes examine the average effective response time in-depth, revealing performance distinctions among wireless fidelity (Wi-Fi) and fourth- and fifth-generation mobile connections. The outcomes illustrate the reliability of voice-activated light control via Google Assistant, with response accuracy rates of 83 percent for Thai voice commands and 91.50 percent for English voice commands. Moreover, the Blynk mobile application provided exceptional precision regarding operating light-button commands. The PIR motion detectors have a one hundred percent detection accuracy, and a 2.5 s delay is advised for PIR detection. Extended PIR detection delays result in prolonged system response times. This study examines the intricacies of response times across various environmental conditions, considering different degrees of mobile communication quality. This study ultimately advances the field by developing an IoT system prepared for efficient integration into everyday life, holding the potential to provide improved convenience, time-saving effectiveness, cost-efficiency, and enhanced home security protocols.

IoT-based Smart Home Security System with Machine Learning Models

The Internet of Things (IoT) has various applications in practice, such as smart homes and buildings, traffic management, industrial management, and smart farming. On the other hand, security issues are raised by the growing use of IoT applications. Researchers develop machine learning models that focus on better classification accuracy and decreasing model response time to solve this security problem. In this study, we made a comparative evaluation of machine learning algorithms for intrusion detection systems on IoT networks using the DS2oS dataset. The dataset was first processed to feature extraction using the info gain attribute evaluation feature extraction approach. The original dataset (12 attributes), the dataset (6 attributes) produced using the info gain approach, and the dataset (11 attributes) obtained by eliminating the timestamp attribute was then formed. These datasets were subjected to performance testing using several machine learning methods and test choices (crossfold-10, percentage split). The test performance results are presented, and an evaluation is performed, such as accuracy, precision, recall, and F1 score. According to the test results, it has been observed that high accuracy detection rates are achieved for IoT devices with limited processing power.

Home Surveillance Monitoring with Esp32-Cam and SD Card For Data Storage

In the last three years the crime rate of theft has increased, to reduce crime this form of theft can be overcome by making a home security system using ESP32-CAM, the system made aims to conduct surveillance that can be seen again the results of images that have been taken by ESP32-CAM, then stored on the SD Card and send notifications to social media. This research uses the R&D (Research &; Development) method or research, and development is a systematic study process to develop and validate products to be used in education. Products developed / produced include training materials for teachers, teaching materials, learning media, questions, and management systems in learning. The result of the implementation of a security system is the stage where the system that has been designed explains the creation of a system in accordance with previous analysis and design. After the implementation stage is carried out, a system test is needed to prove that the application can run properly. The test results that have been done using the android application and Sdcard run well, the PIR sensor can only detect objects as far as 4 meters. With this system, it is expected to be able to provide protection and security for homes, property, and residents. On the other hand, this approach also creates a chance to dig deeper into technology development using ESP32-CAM as an effective and efficient solution to tackle rising crime.

Implementasi Sistem Keamanan Rumah Berbasis Pengenalan Wajah untuk Peningkatan Keamanan Residensial

The objective of this research is to develop a home security system that utilizes facial recognition technology using the Haar Cascade method and ESP32CAM. In the face of criminal threats, the importance of an effective home security system is increasing. The facial recognition method employed in this study is the Haar Cascade method, which utilizes specific facial features to identify individual identities. The ESP32CAM sensor is used as a camera to capture facial images of the home occupants. The research involves the process of collecting facial image data from the home occupants, which is then used to train the facial recognition model. Subsequently, the model is implemented in a home security system that is connected to the internet and equipped with motion sensors. When motion is detected, the ESP32CAM camera captures an image of the visitor's face and compares it with the previously identified facial data. If a match is found, the system grants access to the visitor. The research results demonstrate that the developed home security system achieves accurate facial recognition with a high success rate. Therefore, this system has the potential to enhance home security and provide better protection for its occupants

Performance Analysis of Facial Image Feature Extraction Algorithm for Smart Home Security System Detection

Alongside the development of technology to facilitate multi-family security, security tools are also being developed. Smart home security is one of the very popular security tools in Indonesian home construction. The tool works automatically in real time and has no restrictions on environmental conditions. However, currently available tools still lack consistent accuracy and consistent performance. To solve this problem, the author proposes a smart home security system with an Arduino UNO-connected camera, two relay modules, a magnetic lock, and connecting to a home Internet of Things system. The methods used in the research for this thesis project were: 1. Literature review of ongoing Smart Home Security using facial image feature extraction algorithm research; 2. Deployment of Arduino UNO, 2 Relay Module, and Solenoid Lock; 3. The feature extraction algorithm used is Wavelet. The proposed method is expected to achieve an accuracy of 80% or more. The experimental results showed that the proposed prototype of this experiment achieved the accuracy of 85.7%. In addition to accuracy, there is also precision rate at 87.94%, recall rate at 87.56%, and f1-score rate at 87.28%